End of training

README.md

@@ -1,201 +1,127 @@
 ---
-library_name: transformers
-tags: []
+license: other
+base_model: nvidia/mit-b0
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b0-finetuned-segments-dots-1
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
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-## How to Get Started with the Model
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-## Training Details
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-### Training Data
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-### Training Procedure 
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# segformer-b0-finetuned-segments-dots-1
+
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the rohan8020/test dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0000
+- Mean Iou: 0.0
+- Mean Accuracy: nan
+- Overall Accuracy: nan
+- Accuracy Unlabeled: nan
+- Accuracy Dots: nan
+- Iou Unlabeled: 0.0
+- Iou Dots: 0.0
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 6e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 250
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dots | Iou Unlabeled | Iou Dots |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
+| 0.0029        | 4.0   | 20   | 0.0122          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0004        | 8.0   | 40   | 0.0010          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0003        | 12.0  | 60   | 0.0004          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0003        | 16.0  | 80   | 0.0003          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0003        | 20.0  | 100  | 0.0002          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0002        | 24.0  | 120  | 0.0002          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 28.0  | 140  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0002        | 32.0  | 160  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 36.0  | 180  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 40.0  | 200  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0002        | 44.0  | 220  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 48.0  | 240  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 52.0  | 260  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 56.0  | 280  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 60.0  | 300  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 64.0  | 320  | 0.0001          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 68.0  | 340  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 72.0  | 360  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 76.0  | 380  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 80.0  | 400  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 84.0  | 420  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 88.0  | 440  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0001        | 92.0  | 460  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 96.0  | 480  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 100.0 | 500  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 104.0 | 520  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 108.0 | 540  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 112.0 | 560  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 116.0 | 580  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 120.0 | 600  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 124.0 | 620  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 128.0 | 640  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 132.0 | 660  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 136.0 | 680  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 140.0 | 700  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 144.0 | 720  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 148.0 | 740  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 152.0 | 760  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 156.0 | 780  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 160.0 | 800  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 164.0 | 820  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 168.0 | 840  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 172.0 | 860  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 176.0 | 880  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 180.0 | 900  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 184.0 | 920  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 188.0 | 940  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 192.0 | 960  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 196.0 | 980  | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 200.0 | 1000 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 204.0 | 1020 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 208.0 | 1040 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 212.0 | 1060 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 216.0 | 1080 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 220.0 | 1100 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 224.0 | 1120 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 228.0 | 1140 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 232.0 | 1160 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 236.0 | 1180 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 240.0 | 1200 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 244.0 | 1220 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+| 0.0           | 248.0 | 1240 | 0.0000          | 0.0      | nan           | nan              | nan                | nan           | 0.0           | 0.0      |
+
+
+### Framework versions
+
+- Transformers 4.37.0
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

config.json

@@ -0,0 +1,78 @@
+{
+  "_name_or_path": "nvidia/mit-b0",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 256,
+  "depths": [
+    2,
+    2,
+    2,
+    2
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    32,
+    64,
+    160,
+    256
+  ],
+  "id2label": {
+    "0": "unlabeled",
+    "1": "dots"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "dots": 1,
+    "unlabeled": 0
+  },
+  "layer_norm_eps": 1e-06,
+  "mlp_ratios": [
+    4,
+    4,
+    4,
+    4
+  ],
+  "model_type": "segformer",
+  "num_attention_heads": [
+    1,
+    2,
+    5,
+    8
+  ],
+  "num_channels": 3,
+  "num_encoder_blocks": 4,
+  "patch_sizes": [
+    7,
+    3,
+    3,
+    3
+  ],
+  "reshape_last_stage": true,
+  "semantic_loss_ignore_index": 255,
+  "sr_ratios": [
+    8,
+    4,
+    2,
+    1
+  ],
+  "strides": [
+    4,
+    2,
+    2,
+    2
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0"
+}

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